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Recent progress in AI and Reinforcement learning has shown great success insolving complex problems with high dimensional state spaces. However, most ofthese successes have been primarily in simulated environments where failure isof little or no consequence. Most real-world applications, however, requiretraining solutions that are safe to operate as catastrophic failures areinadmissible especially when there is human interaction involved. Currently,Safe RL systems use human oversight during training and exploration in order tomake sure the RL agent does not go into a catastrophic state. These methodsrequire a large amount of human labor and it is very difficult to scale up. Wepresent a hybrid method for reducing the human intervention time by combiningmodel-based approaches and training a supervised learner to improve sampleefficiency while also ensuring safety. We evaluate these methods on variousgrid-world environments using both standard and visual representations and showthat our approach achieves better performance in terms of sample efficiency,number of catastrophic states reached as well as overall task performancecompared to traditional model-free approaches

Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention